The estimation of the driver's emotional state is a fundamental activity to both improve the quality of driving and increase road safety. Indeed, on the one hand, the deployment of an accurate on-board measurement system for the emotional state of the driver allows the vehicle to tune some on-board settings (e.g. music, the screen brightness or the information given to the pilot). On the other hand, the estimation of the drowsiness of the pilot with such a measurement system can prevent dangerous situations by warning in advance the driver. Starting from an eye tracking system already presented in a previous article, our final project aims at merging information coming from the driver, the vehicle and the environment to properly estimate the emotional state of the driver. This paper discusses about the possibility to use Artificial Intelligence (AI) algorithms and sensor fusion to design a complete driver's emotional and drowsiness state estimation system. The goal of the paper is twofold: firstly, this article aims at reviewing the current state-of-the-art on the field; secondly we want to outline the future improvements for our preliminary setup.

On the Use of Artificial Intelligence and Sensor Fusion to Develop Accurate Eye Tracking and Driver’s Emotional State Estimation Systems

Morato, Alberto;Tramarin, Federico;
2022

Abstract

The estimation of the driver's emotional state is a fundamental activity to both improve the quality of driving and increase road safety. Indeed, on the one hand, the deployment of an accurate on-board measurement system for the emotional state of the driver allows the vehicle to tune some on-board settings (e.g. music, the screen brightness or the information given to the pilot). On the other hand, the estimation of the drowsiness of the pilot with such a measurement system can prevent dangerous situations by warning in advance the driver. Starting from an eye tracking system already presented in a previous article, our final project aims at merging information coming from the driver, the vehicle and the environment to properly estimate the emotional state of the driver. This paper discusses about the possibility to use Artificial Intelligence (AI) algorithms and sensor fusion to design a complete driver's emotional and drowsiness state estimation system. The goal of the paper is twofold: firstly, this article aims at reviewing the current state-of-the-art on the field; secondly we want to outline the future improvements for our preliminary setup.
2022
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
ADAS
Artificial Intelligence
CNN
Computer Vision
Deep Learning
Driving Automation
Drowsiness Estimation
Emotional State Estimation
Eye Tracking
Machine Learning
Sensor Fusion
Vision Based Measurement
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/535354
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